




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def 				A__ (snake_case   :		List[str]		,	snake_case   :		Optional[int]=False				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Any             =      OmegaConf.load(_lowerCamelCase				)
					if display:
										print(yaml.dump(OmegaConf.to_container(_lowerCamelCase				)				)				)
					return config





def 				A__ (snake_case   :		Dict		,	snake_case   :		int=None		,	snake_case   :		List[str]=None				)     ->						str:





					if conf_path is None:
										__UpperCamelCase					:	Dict             =      "./model_checkpoints/vqgan_only.yaml"
					__UpperCamelCase					:	Dict             =      load_config(_lowerCamelCase		,	display=_lowerCamelCase				)
					__UpperCamelCase					:	Any             =      VQModel(**config.model.params				)
					if ckpt_path is None:
										__UpperCamelCase					:	List[str]             =      "./model_checkpoints/vqgan_only.pt"
					__UpperCamelCase					:	Any             =      torch.load(_lowerCamelCase		,	map_location=_lowerCamelCase				)
					if ".ckpt" in ckpt_path:
										__UpperCamelCase					:	Optional[Any]             =      sd["state_dict"]
					model.load_state_dict(_lowerCamelCase		,	strict=_lowerCamelCase				)
					model.to(_lowerCamelCase				)
					del sd
					return model





def 				A__ (snake_case   :		Tuple		,	snake_case   :		Dict				)     ->						Optional[int]:
					__UpperCamelCase					:	Any             =      model.encode(_lowerCamelCase				)
					print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''				)
					__UpperCamelCase					:	List[str]             =      model.decode(_lowerCamelCase				)
					return xrec





def 				A__ (snake_case   :		List[Any]		,	snake_case   :		Any=False				)     ->						List[str]:
					__UpperCamelCase					:	Tuple             =      string.rsplit("""."""		,	1				)
					if reload:
										__UpperCamelCase					:	Tuple             =      importlib.import_module(_lowerCamelCase				)
										importlib.reload(_lowerCamelCase				)
					return getattr(importlib.import_module(_lowerCamelCase		,	package=_lowerCamelCase				)		,	cls				)





def 				A__ (snake_case   :		Optional[Any]				)     ->						str:





					if "target" not in config:
										raise KeyError("""Expected key `target` to instantiate."""				)
					return get_obj_from_str(config["""target"""]				)(**config.get("""params"""		,	{}				)				)





def 				A__ (snake_case   :		Optional[Any]		,	snake_case   :		Tuple		,	snake_case   :		Optional[Any]=True		,	snake_case   :		Union[str, Any]=True				)     ->						Optional[int]:
					__UpperCamelCase					:	Any             =      instantiate_from_config(_lowerCamelCase				)
					if sd is not None:
										model.load_state_dict(_lowerCamelCase				)
					if gpu:
										model.cuda()
					if eval_mode:
										model.eval()
					return {"model": model}







def 				A__ (snake_case   :		int		,	snake_case   :		Any		,	snake_case   :		Union[str, Any]		,	snake_case   :		List[str]				)     ->						Union[str, Any]:





					if ckpt:
										__UpperCamelCase					:	Any             =      torch.load(_lowerCamelCase		,	map_location="""cpu"""				)
										__UpperCamelCase					:	List[str]             =      pl_sd["global_step"]
										print(F'''loaded model from global step {global_step}.'''				)
					else:
										__UpperCamelCase					:	int             =      {"state_dict": None}
										__UpperCamelCase					:	str             =      None
					__UpperCamelCase					:	Union[str, Any]             =      load_model_from_config(config.model		,	pl_sd["""state_dict"""]		,	gpu=_lowerCamelCase		,	eval_mode=_lowerCamelCase				)["model"]
					return model, global_step








from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip